Short answer: In JetBrains’ published comparison, Mellum2.1 Thinking leads Qwen3.5 (9B) on LiveCodeBench v6, HumanEval+, MBPP+, BFCL v4 and WorkBench. Qwen3.5 (9B) leads on SWE-bench Verified, Terminal-Bench 2.1, SWE-bench Pro and ToolHop. That makes Mellum2.1 the stronger result on several code-generation and tool-calling tests, while Qwen3.5 (9B) has the edge on the repository, terminal and software-engineering evaluations most closely resembling long agent runs. There is no universal winner.
The phrase “Qwen Coder” is not a precise checkpoint name in the cited comparison. JetBrains explicitly tested Qwen3.5 (9B) against Mellum2.1 Thinking; the scores below should not be generalized to every model carrying the Qwen or Qwen Coder name. The figures are publisher-reported results, not an independent reproduction. JetBrains’ model card provides the evaluation table and test conditions.
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What is actually being compared?
This comparison has two different levels of precision. Mellum2.1 is a specific JetBrains model family, and the published results identify the evaluated variant as Mellum2.1 Thinking. “Qwen Coder” is a broad reader shorthand rather than the name of one uniquely identified checkpoint. JetBrains’ table names its comparator as Qwen3.5 (9B), so that is the only Qwen model for which the following head-to-head numbers apply.
If you meant another Qwen checkpoint, such as a differently sized or differently tuned coding model, its context window, prompting format, tool support and benchmark scores may differ. Treating the table as a comparison with an unspecified “Qwen Coder” model would overstate what the evidence establishes.
At-a-glance differences
| Model | Published identity | Parameter configuration | Listed context | License or deployment information |
|---|---|---|---|---|
| Mellum2.1 Thinking | JetBrains Mellum2.1, a mixture-of-experts model | 12B total parameters; 2.5B active parameters | 131,072 tokens | Apache 2.0; model card includes a vLLM serving path and tool-calling configuration |
| Qwen3.5 (9B) | The specific Qwen comparator named by JetBrains | 9B, as identified in the comparison | Not stated in the cited comparison | Not stated in the cited comparison |
The 12B and 2.5B figures for Mellum2.1 describe total and active mixture-of-experts parameters, not two separate model editions. Active parameters indicate how many parameters are used for a token through the routing scheme; they should not be read as a direct quality score or as a promise that the model will fit a particular graphics card. The cited material does not specify a minimum GPU, system RAM, quantization, or throughput configuration for full-context serving.
Benchmark results
The following values are the scores reported in JetBrains’ Mellum2.1 model card. A higher number is not automatically better for every engineering task: each benchmark exercises a different combination of generation, repository modification, terminal use, tool calling or agent behavior.
| Evaluation | Mellum2.1 Thinking | Qwen3.5 (9B) | Reported leader |
|---|---|---|---|
| LiveCodeBench v6 | 82.0 | 75.4 | Mellum2.1 Thinking |
| HumanEval+ | 91.5 | 89.6 | Mellum2.1 Thinking |
| MBPP+ | 79.4 | 69.8 | Mellum2.1 Thinking |
| SWE-bench Verified | 47.0 | 50.0 | Qwen3.5 (9B) |
| Terminal-Bench 2.1 | 17.4 | 21.7 | Qwen3.5 (9B) |
| SWE-bench Pro | 28.0 | 38.0 | Qwen3.5 (9B) |
| BFCL v4 | 62.3 | 58.5 | Mellum2.1 Thinking |
| WorkBench | 44.6 | 39.7 | Mellum2.1 Thinking |
| ToolHop | 49.1 | 52.0 | Qwen3.5 (9B) |
JetBrains identifies these as model-card results, not a neutral third-party leaderboard. The same page notes that its non-agentic evaluations used greedy decoding. Agentic evaluations used one open-source harness, Pi v0.73.1, with shell and file tools, a 114K-token context and a maximum of 16K tokens per turn. Mellum2.1 used its default temperature of 1.0. JetBrains also says Mellum2 Thinking was re-evaluated with this pipeline, which is why these numbers can differ slightly from an earlier technical report.
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Code-generation tests favor Mellum2.1
Mellum2.1’s largest listed advantages are on LiveCodeBench v6, HumanEval+ and MBPP+. Those results suggest a strong showing on standalone programming problems and generated solutions under the published decoding setup. The gaps are especially visible on MBPP+ (79.4 versus 69.8) and LiveCodeBench v6 (82.0 versus 75.4).
Such scores are useful when your agent frequently writes a function, proposes a patch, or solves a bounded problem before handing control back to a human. They do not by themselves measure whether the model can navigate an unfamiliar monorepo, recover from a failed command, or keep a multi-step change consistent.
Repository and terminal evaluations favor Qwen3.5 (9B)
Qwen3.5 (9B) scores higher on SWE-bench Verified (50.0 versus 47.0), Terminal-Bench 2.1 (21.7 versus 17.4) and SWE-bench Pro (38.0 versus 28.0). These are the most consequential differences if your coding agent must inspect an existing codebase, use a shell, modify several files and validate a fix through a longer sequence of actions.
The table does not prove that Qwen3.5 will win every repository task. It shows a lead under JetBrains’ particular harness, context allocation, tools and prompting pipeline. Your repository language mix, test quality, command permissions and stopping policy can change the practical outcome.
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Mellum2.1 leads BFCL v4 at 62.3 versus 58.5 and WorkBench at 44.6 versus 39.7. Qwen3.5 leads ToolHop at 52.0 versus 49.1. These results point to different strengths across tool-selection and multi-step tool-use tests rather than a single model that dominates all tool workflows.
What Mellum2.1 is designed to do
JetBrains describes Mellum2.1 as a 12B mixture-of-experts model with 2.5B active parameters, released under the Apache 2.0 license. Its stated target is coding agents, sub-agents and self-hosted use. The model card lists a 131,072-token context length and documents a vLLM serving route that includes tool-calling configuration. See the model card for the current serving details.
According to JetBrains’ announcement, post-training—primarily reinforcement learning—was the main change in Mellum2.1. The training mix covered software engineering, tool use, mathematics, competitive programming and science tasks. JetBrains says the intended workflow is for the model to explore a codebase, edit files and check its own changes. Those are the publisher’s design and training descriptions; they are not independent hands-on findings.
The Apache 2.0 license and self-hosting position matter to teams that need control over where source code and tool outputs run. They do not remove operational work: you still need an inference server, a compatible runtime, memory headroom, authentication, tool sandboxing, logging and a policy for untrusted repository code.
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The cited material documents the model’s serving path but does not publish a universal hardware recipe. A practical deployment should therefore be treated as an integration exercise rather than a guaranteed one-command installation.
- Identify the exact checkpoint. Use the Mellum2.1 Thinking model identified in the model card, rather than assuming that another Mellum release has identical prompts or weights.
- Review the license and model instructions. The listed license is Apache 2.0. Check the current model card for any prompt format, tokenizer, special tool tokens or safety guidance before wiring it into an agent.
- Start from the documented vLLM path. The model card shows the supported serving pattern and a tool-calling configuration. Keep the server and model versions aligned with that documentation instead of copying an example meant for a different checkpoint.
- Expose only the tools your agent needs. For repository work this commonly means controlled file reads and writes plus a shell runner. Run commands in a sandbox, restrict network access where appropriate, and require confirmation for destructive operations.
- Set context and turn limits deliberately. JetBrains lists 131,072 tokens as the model context, but its published agent tests used 114K and capped each turn at 16K. A server that accepts the nominal maximum may still run out of memory or become slow at that setting.
- Measure your workload. Record completion rate, tests passed, tool-call errors, latency, tokens per second, retries and context usage on your own repositories. Those measurements are more actionable than transferring a leaderboard rank directly to production.
No cited source specifies the GPU count, VRAM, CPU, quantization level or maximum concurrent users required to serve Mellum2.1 at its listed context. Do not infer a hardware purchase from the 12B or 2.5B parameter figures alone.
Speed and throughput claims
JetBrains’ announcement reports that, under heavy load, Mellum2.1 served almost twice as many tokens as Qwen3.5-9B. It also reports that multi-token prediction made a single request about 1.6 times faster. The accessible announcement text does not provide the complete plotted measurements, hardware configuration, batch size, prompt length, output length or statistical treatment behind those claims.
Use those figures as JetBrains’ reported performance claims, not as independently established benchmarks. Throughput under concurrency and latency for one request answer different questions: a shared service may benefit from higher aggregate tokens per second, while an interactive coding agent may care more about time to the first useful tool call and time between consecutive actions.
The Tool Desk
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- identical prompt and tool schemas;
- the same input repository and context truncation policy;
- the same maximum output tokens and stopping rules;
- the same hardware, quantization and server version;
- the same concurrency levels, from one request to the expected peak;
- separate measurements for time to first token, complete response latency and aggregate throughput.
Which model should you choose?
| Your priority | Better starting point from the published comparison | Why | What to verify locally |
|---|---|---|---|
| Standalone code generation and benchmark-style programming problems | Mellum2.1 Thinking | Higher LiveCodeBench v6, HumanEval+ and MBPP+ scores | Language coverage, formatting reliability and test-pass rate on your code |
| Repository issue resolution with shell and file operations | Qwen3.5 (9B) | Higher SWE-bench Verified, Terminal-Bench 2.1 and SWE-bench Pro scores | Recovery after failed commands, patch scope and behavior on your repository structure |
| Structured function or tool calling | Start with Mellum2.1, then A/B test Qwen3.5 | Mellum2.1 leads BFCL v4, while Qwen3.5 leads ToolHop | Schema adherence, argument validity, retries and multi-tool sequencing |
| Self-hosting under an Apache 2.0 model license | Mellum2.1 | The cited model card identifies Apache 2.0 and documents self-hosted serving | Runtime compatibility, memory, security controls and your organization’s policy |
| High-concurrency serving | Benchmark both; Mellum2.1 has a publisher-reported advantage | JetBrains reports almost twice the token throughput under heavy load | Reproduce the workload with your hardware and concurrency target |
For a general coding assistant, a sensible first trial is Mellum2.1 when tool-call correctness, bounded code generation or self-hosted licensing is the main concern. Start with Qwen3.5 (9B) when the agent’s central job is to resolve repository issues through terminal-heavy, multi-step work. If the same system must do both, route a small evaluation set through each model before committing to one default.
A practical evaluation plan for your agent
Build a representative task set
Collect tasks from the work your team actually performs: a small function change, a cross-file feature, a failing test, a dependency update, a command-line diagnosis and a request that requires a structured tool call. Include both clean repositories and cases with misleading logs or incomplete documentation.
Define success before running the models
- Tests pass without manual edits.
- The agent changes only authorized files.
- Tool arguments validate against the schema on the first attempt or within a fixed retry budget.
- The final explanation accurately describes the change and remaining risks.
- Latency and token use stay within the user experience and cost limits you set.
Keep the harness identical
Use the same system prompt, repository snapshot, tool descriptions, permissions, context budget, maximum turns and temperature policy. Changing those variables can make a model comparison measure the harness rather than the model. The JetBrains comparison itself attempted to standardize agentic tests with Pi v0.73.1, shell and file tools, 114K context and up to 16K tokens per turn; matching that setup is not required, but documenting your differences is.
Inspect failures, not only pass rates
Classify failures as misunderstood requirements, incorrect code, invalid tool calls, terminal misuse, context loss, premature stopping, test misunderstanding or infrastructure error. A model with a slightly lower aggregate score may be preferable if its failures are easy to detect and recover from, while a higher-scoring model may be unsuitable if it makes unsafe changes or consumes the entire context before testing.
Important limitations of the published comparison
- Checkpoint ambiguity: the evidence compares Mellum2.1 Thinking with Qwen3.5 (9B), not an undefined Qwen Coder category.
- Publisher-reported numbers: JetBrains supplies the table and test description; the figures are not an independent reproduction.
- Task dependence: different evaluations produce different leaders, so no single score establishes a universal best model.
- Decoding differences: non-agentic tests used greedy decoding, while Mellum2.1 used a default temperature of 1.0 in the agentic setup.
- Harness effects: agent scores reflect Pi v0.73.1, the specified tools, context limits and turn cap as well as the model itself.
- Hardware uncertainty: the cited sources do not state the hardware requirements needed to realize the 131,072-token context or the reported speed claims.
- Speed evidence: the announcement gives headline throughput and single-request acceleration claims without exposing all plotted measurement details in the accessible text.
Bottom line: pick by workflow, then validate
Mellum2.1 Thinking is the more compelling starting point for code-generation quality, several tool-calling measures and an Apache 2.0 self-hosted deployment. Qwen3.5 (9B) is the stronger starting point for the repository and terminal benchmarks in JetBrains’ table. Because the leaders switch across evaluations—and because “Qwen Coder” does not identify one exact checkpoint—your final choice should come from a controlled test on the agent tasks, tools, repositories and hardware you intend to run.
For the model’s current specifications, serving instructions and complete evaluation table, consult the Mellum2.1 Thinking model card. JetBrains’ product and training rationale is described in Mellum2.1 Gets to Work: A Fast Open Model for Coding Agents; predecessor context appears in Mellum2 Goes Open Source.
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